Selecting What Matters: Semantic Compression-Guided Selective Pooling for Long-Context Embeddings

arXiv:2609.37782 · cs.CL · Submitted 2026-09-29 · Read on arXiv

cs.CL

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Embeddings — 34× in this paper · explained in 2 episodes
• Aggregation — 13× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• Token Prepending — 6× in this paper
• Generalizability — 4× in this paper · explained in 6 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Hierarchical Token Prepending — 3× in this paper
• Semantic Compression-guided Selective Pooling — 3× in this paper
• Ablation study — 2× in this paper · explained in 1 episode
• Clustering — 2× in this paper · explained in 2 episodes
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Training-Free Framework — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Contextual Understanding — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Inference-Time Steering — 1× in this paper · explained in 2 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Zero-Shot Generalization — 1× in this paper · explained in 5 episodes

Sources

• LMK > CLS: Landmark Pooling for Dense Embeddings
• The Llama 3 Herd of Models
• Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents
• Mistral 7B
• Gemma 2: Improving Open Language Models at a Practical Size
• Simple Techniques for Enhancing Sentence Embeddings in Generative Language Models

Related papers

• Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
• Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
• Subliminal Steering: Stronger Encoding of Hidden Signals
• MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
• The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
• Untangling the Mechanisms of Misleading Context in Medical Question Answering